Stylistic Dialogue Generation Based on Character Personality in Narrative Films.

Traditional narrative systems consist of two steps of process, story generation and discourse generation. However, many interactive systems make more effort on story generation rather than discourse generation. For discourse generation, dialogue is an important way used to unfold story and reveal characters in stories, and it is reasonable to expand the capability of narrative system by exploring the potential of dialogue generation in narratives. Also, Recent research in conditional dialogue generation is mostly focusing on the context of natural conversation generation with speakers’ profile information. While incorporating the styles that relevant to narratives is yet to be widely investigated. According to the research made, in this document, we propose an approach using a pre-trained language model, in order to explore the potential of generating dialogues with embedded narrative-related features within the context of narrative films. In this approach, three different embedding methods are leveraged to incorporate Big-Five personalities of characters into transformer-based neural networks, training on a new corpus, which is created and well-parsed from screenplays. We conduct experiments using both automatic metrics and human evaluation to measure the quality of the generated dialogue and personality identification accuracy. All the dialogues for evaluation and analysis are generated with settings of the perspectives of embedding method, personality trait, personality level, and film genre, which is to explore the impact of different setting on dialogue generation with additional narrative-related styles. According to the automatic experimental results, we demonstrate that our approach is able to generate dialogues with increased variety. Also overall, the generated dialogues are able to correctly reflect the given target personality. We also conduct three user studies for evaluate dialogues with human judgements. In the first and the second user study, we evaluate the dialogues generated with film- level personality using CTE (Combined Textual Embedding) embedding method. The results show that human participants are inclined to perceive one extreme end of each personality trait. In the third user study, we evaluate generated dialogues with all setting combinations synthetically. Overall, the results show that target personalities can be identified with various degrees of accuracy. Also, a negative correlation between personality identification accuracy and dialogue quality is observed. In this thesis, we propose a new approach for stylistic dialogue generation and demonstrate its effectiveness. We believe the observations and discoveries could be a start and a tryout to apply deep learning technique and big data to boost narrative dialogue generation. And we also believe that our research can be applied in plenty of potential scenarios, such as helping the authors creating huge amount of conversations between different characters by popping utterance options corresponding to the character settings

Influence of Personality-based Features for Dialogue Generation in Computational Narratives

In this paper, we present an approach for generating dialogues for characters within the context of computational narratives using personality-based features for deep neural networks. The approach integrates the requirements of both narrative genres and personality traits for the definition of character-based stylistic models. The modelling of characters’ features from existing datasets of complete stories permits the generation of personality-rich character dialogues. We present early results from an evaluation based on a sample of characters’ personality traits across different narrative genres, demonstrating variability in the resulting dialogue

Faster Ray Tracing through Hierarchy Cut Code

We propose a novel ray reordering technique to accelerate the ray tracing process by encoding and sorting rays prior to traversal. Instead of spatial coordinates, our method encodes rays according to the cuts of the hierarchical acceleration structure, which is called the hierarchy cut code. This approach can better adapt to the acceleration structure and obtain a more reliable encoding result. We also propose a compression scheme to decrease the sorting overhead by a shorter sorting key. In addition, based on the phenomenon of boundary drift, we theoretically explain the reason why existing reordering methods cannot achieve better performance by using longer sorting keys. The experiment demonstrates that our method can accelerate secondary ray tracing by up to 1.81 times, outperforming the existing methods. Such result proves the effectiveness of hierarchy cut code, and indicate that the reordering technique can achieve greater performance improvement, which worth further research

Biomimetic Z-scheme photocatalyst with a tandem solid-state electron flow catalyzing H_2 evolution

Similar to natural photosynthetic systems, artificial photosynthetic systems require synergistic cooperation between light harvesting, charge separation and redox catalysis. Herein, a three-dimensional (3D) hierarchical photocatalyst is designed with a novel Z-scheme two-photon excitation, defined by the complementary absorption of higher energy and lower energy photons by cadmium sulfide nanowires (CdS NWs) and cobalt–benzimidazole (Co-bIm) coordination polymers (CBPs), respectively. Without any noble-metal co-catalyst, the microscopically integrated CdS–CBP photocatalysts demonstrated dramatically enhanced photocatalytic activities of H_2 evolution, which were up to 10.6 folds higher than those of pristine CdS NWs. Structurally, the intimate interfacial contact between the 3D CdS NW scaffold and the discrete CBP microstructures benefits their strong electronic interaction and efficient charge separation. Upon simultaneous light excitation, a tandem solid-state electron flow from CdS to CBP and then from metal (Co) to ligand (bIm) precisely catalyzes the reduction of pre-activated H atoms on the bIm ligands for efficient H_2 evolution

Biomimetic Z-scheme photocatalyst with a tandem solid-state electron flow catalyzing H_2 evolution

Similar to natural photosynthetic systems, artificial photosynthetic systems require synergistic cooperation between light harvesting, charge separation and redox catalysis. Herein, a three-dimensional (3D) hierarchical photocatalyst is designed with a novel Z-scheme two-photon excitation, defined by the complementary absorption of higher energy and lower energy photons by cadmium sulfide nanowires (CdS NWs) and cobalt–benzimidazole (Co-bIm) coordination polymers (CBPs), respectively. Without any noble-metal co-catalyst, the microscopically integrated CdS–CBP photocatalysts demonstrated dramatically enhanced photocatalytic activities of H_2 evolution, which were up to 10.6 folds higher than those of pristine CdS NWs. Structurally, the intimate interfacial contact between the 3D CdS NW scaffold and the discrete CBP microstructures benefits their strong electronic interaction and efficient charge separation. Upon simultaneous light excitation, a tandem solid-state electron flow from CdS to CBP and then from metal (Co) to ligand (bIm) precisely catalyzes the reduction of pre-activated H atoms on the bIm ligands for efficient H_2 evolution

Application of the ultrasonic technology for quantitative measurements of elastic properties of materials

Presently, ultrasonic non-destructive testing technique is widely and confidently used in many different fields. In this project, ultrasonic technology is used to determine the physical properties of materials. In the first part of the project, the V(z) technique is presented to determine the Young’sModulus nad Raileigh velocity of isotropic materials.Master of Science (Mechanics & Processing of Materials

Downregulation of hTERT: an important As2O3 induced mechanism of apoptosis in myelodysplastic syndrome.

Two myelodysplastic syndrome (MDS) cell lines, MUTZ-1 and SKM-1 cells, were used to study the effect of arsenic trioxide (As2O3) on hematological malignant cells. As2O3 induced this two cell lines apoptosis via activation of caspase-3/8 and cleavage of poly (ADP-ribose) polymerase (PARP), a DNA repair enzyme. As2O3 reduced NF-κB activity, which was important for inducing MUTZ-1 and SKM-1 cells apoptosis. As2O3 also inhibited the activities of hTERT in MUTZ-1 and SKM-1 cells. Moreover, the NF-κB inhibitor, pyrrolidine dithiocarbamate (PDTC), had no effect on caspase-8 activation, although PDTC did inhibit MUTZ-1 and SKM-1 cells proliferation. Incubation of MUTZ-1 cells with a caspase-8 inhibitor failed to block As2O3-induced inhibition of NF-κB activity. Our findings suggest that As2O3 may induce apoptosis in MUTZ-1 and SKM-1 cells by two independent pathways: first, by activation of caspase-3/8 and PARP; and second, by inhibition of NF-κB activity, which results in downregulation of hTERT expression. We conclude that hTERT and NF-κB are important molecular targets in As2O3-induced apoptosis

The complete mitochondrial genome of Amur ide (Leuciscus waleckii waleckii)

In this study, the complete mitochondrial genome of Leuciscus waleckii waleckii was sequenced and got a whole length of 16605 bp. This genome was contain 2 rRNA, 22 tRNA, 13 protein-coding genes, 1 control region (D-loop) and 1 replication origin. And the nucleotide composition of this mitochondrial genome is 27.72% for A, 26.28% for T, 27.23% for C and 18.77% for G. To clarify the phylogenetic relationship of the Leuciscus waleckii waleckii, we concluded the phylogenetic tree using 12 PCGs (except ND6) of mitochondrial genome in Leuciscus waleckii waleckii and 16 other cyprinid fish by Bayesian inference (BI) methods and maximum-likelihood (ML). And the result show that Leuciscus waleckii waleckii was close to other Leuciscus species, especially Leuciscus baicalensis

A Deep‐Learning Approach for Low‐Spatial‐Coherence Imaging in Computer‐Generated Holography

The low‐spatial‐coherence imaging capability of computer‐generated holography (CGH) is a key to high‐resolution displays, virtual reality, augmented reality, and holographic microscopy. The low spatial coherence caused by complex disturbances can damage the image quality irreversibly. The optical field with low spatial coherence has large fluctuations, making it difficult to be quantified and modeled directly. To tackle these challenges, a deep neural network‐based model U‐residual dense network (U‐RDN) is proposed, which obtains the optimal solution under the low‐spatial‐coherence condition. The large‐scale images are generated using optical experiments, with analysis and restoration by deep learning. Extensive experiments demonstrate the strong out‐of‐distribution robustness of U‐RDN, which is generalizable to unseen classes in unseen domains. The learning‐based approach and low‐spatial‐coherence dataset open a new path toward the next generation of CGH